In geometric processing community, low dimensional embeddings are frequently used to transform the input data from its
original domain to another domain. The main purpose of these embeddings is to reduce the dimensionality of the problem,
preserving the information of the original data in such a way that the solution of the new problem is cheaper and easier.
The segmentation problem can also be considered as a clustering problem, which is frequently solved applying
k-means method, [1]}. In this context, dimensionality reduction for k-means is strongly connected with
low rank approximation of the matrix containing the data to be clustered [2]}. In our problem, the matrix containing
the information about “data points” is the affinity matrix \(W\) . Each row of \(W\)  represents the affinity between a triangular
face and the rest of faces. Hence, a valid strategy to solve the segmentation problem consists in computing a low rank
approximation of the affinity matrix \(W\)  and clustering its rows.
